The expansion of the Internet has revolutionized the ways in which sales transactions are conducted. Buyers and sellers are no longer tied to traditional retail-based or catalog-based transactions. At any given time, millions of transactions are conducted worldwide via the Internet on multiple web sites. Web sites include those associated with one or more sellers advertising and selling their products for buyers visiting the sites, such as Amazon.com®, Yahoo® Shopping and eBay®, as well as those associated with both sellers offering products and services for sale and buyers looking for products and services on messages posted on the sites, such as Craigslist.com®.
Web sites in which sales transactions are conducted can be referred to as “electronic marketplaces” generally characterized by fast, efficient, and easy shopping experiences for buyers. Such electronic marketplaces have become so prevalent that they have at times replaced traditional forms of shopping for certain products and services. Besides offering the convenience of shopping online at any time of day, electronic marketplaces can also provide buyers with a vast array of services that can enhance the shopping experience, including, for example, instant product reviews, feedback from other buyers, and product recommendations, among others.
When buyers purchase products in an electronic market, they can assess and likely pay not only for the products they wish to purchase, but also for a set of fulfillment characteristics. These fulfillment characteristics include, for example, packaging, timeliness of delivery, the extent to which the product description matches the actual product, and the reliability of settlement.
In traditional retail-based transactions, the buyers have a deterministic way of assessing the quality of such fulfillment characteristics. For example, the buyers can physically visit a traditional retail-based outfit and inspect the product and packaging before shipment and settle the transaction immediately after purchase. In the electronic markets, however, fulfillment characteristics may not be reliably described or verified in advance of a product purchase. Such markets generally rely on reputation systems to ensure their viability and efficiency, and to substitute for the trade processes usually taken for granted in traditional transactions. Such markets also may have user-generated qualitative information, such as product reviews and assessments, regarding the products sold in the marketplace.
Reputation systems in the electronic markets are typically represented by a “reputation profile” that provides the potential buyers with information regarding: (a) the number of transactions a seller has successfully completed; (b) a summary of scores (or ratings) from buyers who have completed transactions with the seller in the past; and (c) a chronological list of textual feedback provided by these buyers.
A casual observation of the electronic markets can suggest that different sellers in these markets derive their reputation from different characteristics. For example, some sellers have a reputation for fast delivery, others have a reputation for having the lowest price among their peers, some are praised for their packaging, while others are appreciated for selling high-quality goods but are criticized for being rather slow with shipping. This information, which can be valuable to buyers who may be heterogeneous in the relative value they place on each of the fulfillment characteristics of a transaction, is often contained in textual feedback that may not be structured, and thus this information may not be readily accessible.
Previous reputation systems have focused mostly on their impact on market outcomes using a single number to characterize a seller, such as the average numerical score attributed to the seller and reported by prior buyers, without any significant investigation on how the valuable information embedded in the textual feedback may actually affect the market outcomes. Such previous systems may have used hedonic regressions of absolute price that generally view reputation as a product characteristic to show that the buyers pay more to the sellers who have better histories.
For example, Kalyanam, K., et al., “Return on reputation in online auction market,” Working Paper 02/03-10-WP, Leavy School of Business, Santa Clara University, 2001, presents the effects of online reputation systems on the price of Palm Pilots and PDAs, and Melnik, M. I., et al., “Does a seller's reputation matter? Evidence from eBay auctions,” Journal of Industrial Economics, 50(3), pp. 337-350, 2002, describes the effects of online reputation systems on the price of gold coins. Both of these publications describe that positive feedback from online buyers increases prices, while negative feedback decreases prices.
However, not all of the results in systems and publications are consistent. For example, Eaton, D., “Valuing information: evidence from guitar auctions on eBay,” Working Paper # 0201, Department of Economics, Murray State University, 2002 describes the results which show that positive feedback from online buyers has no effect on the probability of sale or price of electric guitars and negative feedback reduces the probability of sale only for sellers with more than 20 feedback postings. Ghose, A. et al., “Effect of electronic secondary markets on the supply chain,” Journal of Management Information Systems, 22(2), pp. 91-120, 2005, describes the results which show that the online reputation of sellers has no significant or consistent impact on the prices of used books.
The lack of consistency in the results described in these and other prior publications on the effects of reputation systems on the electronic market outcomes may be explained by a more robust measure of the value of reputation (e.g., a price premium, rather than simply absolute price) and a further exploration of the dimensions of reputation that may be found in a specific kind of qualitative information source, i.e., textual feedback. Pavlou, P. et al., “Psychological contract violation in online marketplaces: antecedents, consequences, and moderating role,” Information Systems Research, 16(4), pp. 272-299, 2005 describes that textual feedback is shown to influence price premiums and affect psychological contract violation in online marketplaces. The role of textual feedback in building buyers' trust on the eBay® online marketplace and how it affects price premiums is further described in Pavlou, P. et al., “The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building and Price Premiums,” published at http://www.agsm.ucr.edu/faculty/pages/pavlou.html, 2006.
These publications, however, rely on a manual and expensive content analysis of textual feedback that likely identify only two trust dimensions: i.e., credibility and benevolence. As a result, only a small proportion of text comments are categorized as providing evidence of a seller's outstanding credibility and benevolence. Independent of the differences in their preferences for fulfillment characteristics, the buyers may vary in the way they access and score a transaction. Text-based descriptions of a transaction quality can therefore augment and increase the richness of the information contained in numerical reputation scores. The full extent of the effects of the textual feedback in the outcomes of electronic marketplaces has not yet been determined.
There also exists a wide variety of other qualitative information that, appropriately structured and quantified, can explain other observable economic outcomes. For example, textual information posted by travelers on web-based travel sites can contain information that may explain the prices of hotels at a destination, or the volume of tourist travel to that destination. For example, qualitative feedback from consumers about the quality of products may contain information that explains their demand or total revenue. For example, reviews of restaurants posted by prior diners may contain information that explains the prices charged by these restaurants, or the fraction of tables that are occupied on average. For example, audio recordings of customer service calls may contain information that explain the rate at which customers return products to a merchant, or the rate of repeat purchases from the merchant.
Thus, there remains a need to provide a model, system, method, software arrangement and computer-accessible medium for identifying different dimensions of textual and other feedback in online reputation systems, and characterizing their influence on the pricing power of sellers.